In-processing techniques modify the learning algorithm, for example, by modifying the loss function [1]}, [2]}. Related to the disentanglement part of our algorithm are adversarial approaches [3]}, [4]}, [5]}.  [3]}, for instance, generate an embedding by applying both an adversarial objective and a classification loss. Since the labels might be correlated with the sensitive attribute, such classification loss could reduce fairness to increase accuracy. Our disentanglement approach applies a similar adversarial objective, where the classification loss is only optionally applied to adjust the tradeoff between accuracy and fairness.
